Relationship between Land Surface Temperature and Land Use in Nakhon Ratchasima City, Thailand

The relationship between land surface temperature (LST) and land use in Nakhon Ratchasima was studied using data gathered from three satellite images from Landsat-5 (30th January 1992), Landsat-8 (9 May 2016) and THEOS (17th February 2016). There were four categories of land use: built-up area, green area, bare land and water sources. The split-window concept was used to estimate the LST. In 1992, Nakhon Ratchasima city in Thailand comprised 47.76% built-up area, 37.45% green area, 13.19% bare land and 1.60% water sources. By 2016, the built-up area had increased by 23.04%, the green area had decreased by 16.66%, bare land had decreased by 6.81%, but water sources had increased by 0.43%.  Moreover, in 1992 the mean LST was 25.43 °C for built-up areas, 24.44 °C for green areas, 24.97 °C for bare land and 24.75 °C for water sources. However, by 2016 the LSTs had increased for each category: 28.74 °C for built-up areas (+3.31 °C), 27.20 °C for green areas (+2.76 °C), 28.11 °C for bare land (+3.14 °C) and 27.72 °C for water sources (+2.97 °C). The findings indicated that the LSTs increased with the pace of urbanization and changes in land use. Linear regression analysis revealed that built-up land had a positive correlation with LST, where a 1% increase in built-up area increased its LST by 0.146594 °C

Atmospheric water vapor retrieval from Landsat 8 and its validation

This objective of this paper is to estimate atmospheric water vapor (m)) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. Model analysis showed that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.45g/cm(2) for most atmospheric moisture conditions. The MSWCVR was evaluated by using AERONET ground-measured data and cross-compared with MODIS products in 2013 at forty two ground sites, and results presented that the retrieved wv from TIRS data was highly correlated with but generally larger (about 1.0 g/cm(2)) than two others. The reasons for this uncertainty were mainly ascribed to data systematic noise and radiative calibration error. Future work must pay more attention to the data quality and radiative calibration of Landsat 8 TIRS data.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000349688104083&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Engineering, Electrical & ElectronicGeosciences, MultidisciplinaryRemote SensingEICPCI-S(ISTP)